Our client is an American multinational delivery services company that faced major operational challenges, including shipment delays, incorrect documentation and customer dissatisfaction stemming from package content misclassification. These issues were negatively impacting service reliability and growth. They sought an AI-driven solution to address this, improve shipment classification and reduce delays. Partnering with us, the client pursued a strategic transformation focused on establishing a big data platform, automating model testing and developing tools to process vast volumes of text data. This initiative resulted in reduced manual efforts, enhanced scalability, improved processing design and decreased development time for new models.
The Challenge
Overcoming logistic obstacles hindering growth
The logistics services company encountered issues such as shipment delays, incorrect documentation and frequent customer dissatisfaction due to package misclassification. These problems were exacerbated by inaccuracies in Harmonized Commodity Description and Coding System (HS) mappings, which led to delayed freight delivery and a poor customer experience.
The Objective
A roadmap for operational excellence and enhanced customer satisfaction
The main objective was to implement an AI-powered solution that could proactively predict shipment classification, reducing delays and improving customer experience. The client also aimed to adopt an MLOps framework to automate the building, testing, deploying and governing processes for the AI solution.
The Solution
Strategic advancements utilizing machine learning
We collaborated with the client to streamline their shipment process through a strategic transformation. Key elements of the solution included:
- Big data platform setup: We established a scalable big data platform capable of efficiently processing large data volumes, forming the foundation for iterative learning and AI infrastructure
- Model testing and comparison automation: Automated workflows were implemented to streamline model testing and comparison, significantly reducing the time required to evaluate and select effective models
- Model management and orchestration: A comprehensive model management and orchestration framework was devised across multiple environments. Continuous integration and deployment pipelines ensured seamless model deployment and monitoring, along with a Model-as-a-Service approach for diverse business needs
- Handling high volumes of text: We developed tools, processes and infrastructure designed to process and manage large amounts of text data, improving the accuracy and efficiency of shipment classification
- Advanced model development: Our team employed advanced techniques such as semantic analysis, named entity recognition, ontologies and topic modeling to build robust machine learning models. These models enhanced classification accuracy, addressing limitations in rule-based systems
- Feature store implementation: To boost team productivity and streamline governance, a feature store was established, providing reusable features and ensuring consistent governance practices across the AI solution lifecycle
The Impact
Enhanced global scalability, efficiency and customer experience
The adoption of machine learning models led to several impactful results:
- Reduced manual efforts: Machine learning models significantly reduced manual shipment classification efforts, improving efficiency and eliminating potential errors
- Global scalability: The solution progressed from proof-of-concept to full production within a client-supported big data environment, delivering consistent service and customer experience across regions
- Improved processing design: High-performance processing design was implemented to support real-time workflows, allowing the AI solution to handle operational demands efficiently
- Decreased development time: By employing entity profiling and data orchestration methodologies, the client reduced the time needed to develop new machine learning models, enhancing agility in addressing evolving needs
